This dataset contains 119390 observations for a City Hotel and a Resort Hotel. Each observation represents a hotel booking between the 1st of July 2015 and 31st of August 2017, including booking that effectively arrived and booking that were canceled.
Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted.Four Columns, 'name', 'email', 'phone number' and 'credit_card' have been artificially created and added to the dataset.
The data is originally from the article Hotel Booking Demand Datasets, written by Nuno Antonio, Ana Almeida, and Luis Nunes for Data in Brief, Volume 22, February 2019.
It would be nice for the hotels to have a model to predict if a guest will actually come.
This can help a hotel to plan things like personel and food requirements.
Maybe some hotels also use such a model to offer more rooms than they have to make more money... who knows...
This project is a general project to explore and analyze this data without having a major problem to solve, so you will find that I worked on most of the variables and the relationships between them to explore and analyze most of the data
Here i will exploring the data by answer some question like :
questions¶
- Is there relationship between having babies and the rate of cancellation ? (Statistically)
- Is cancellation of reservations highest in the resort about the city hotel? (Statistically)
- what the kind of hotel that have highest reservations?
- Wich year has the highest number of bookings ?
- Distribution of staye in week nights on count of days
- Distribution of staye in weekend nights on count of days
- DISTRIBUTION OF count leaad time for reservations
- What is the most kind of Board on Reservations ?
- What are the countries with the highest bookings?
Text(0.5, 1.0, 'count of reservvations on every hotel')
Text(0.5, 1.0, 'count of reservations for every year')
('1/1/2015', '9/9/2017')
Text(0.5, 1.0, 'Distribution of staye in week nights')
<AxesSubplot:xlabel='stays_in_weekend_nights', ylabel='count'>
Text(0.5, 1.0, 'DISTRIBUTION OF count leaad time for reservations')
SC (Self Catering) : No meals are included; however, your accommodation will be provided with catering facilities for you to cook light meals.
BB (Bed and Breakfast) : Breakfast is included.
HB (Half Board) : Breakfast and evening meals are included. In some cases, you can choose to receive lunch instead of breakfast – the hotel will confirm this on arrival.
FB (Full Board) : Breakfast, lunch and evening meals are included.
Text(0.5, 1.0, ' the most kind of Board on Reservations')
Text(0.5, 1.0, ' the countries with the highest bookings')
Text(0.5, 1.0, 'The difference in the average cancellation of reservations between years ')
<AxesSubplot:xlabel='arrival_date_month'>
Portugal is the most booked country
the rate of cancellation of reservation increasees every year but at simple rates